Data Driven Understanding of Energy-Meteorological Variability and its Impact on Energy System Operations

Earth’s climate is changing. For a habitable planet in the future the emission of greenhouse gasses needs to be stopped. As future societies still require energy for their basic needs, a transition away from fossil fuel to renewable energy sources is needed. Nothing is as variable as the weather, and weather is the driving force of renewable energy resources. The interaction of societal and weather driven variability, here coined the energy-meteorological variability, is still largely unchartered. This variability is the central theme of this thesis. The different backgrounds and expertise of those working at the intersection of the energy and climate domain mean that the current methods to assess this variability in energy system operations are inadequate. A data driven approach is needed to incorporate the energy-meteorological variability within assessments of (future) energy systems. In this thesis we investigate data driven methods and metrics to quantify and identify a deviation of the expected patterns. We need to overcome the disconnect between energy and climate scientists in order to integrate an understanding of variability in energy system operations. The applicability of approaches in operational energy system assessments is key. Intensive and sustainable collaborations between the different disciplines is needed to facilitate the energy transition, between the different domains of science as well as between science and industry.